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Apache Spark - Lightning-Fast Cluster Computing
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Lightning-fast cluster computing
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<li><a href="http://shark.cs.berkeley.edu">Shark (SQL)</a></li>
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<h5>Latest News</h5>
<ul class="list-unstyled">
<li><a href="/news/spark-0-9-0-released.html">Spark 0.9.0 released</a>
<span class="small">(Feb 02, 2014)</span></li>
<li><a href="/news/spark-0-8-1-released.html">Spark 0.8.1 released</a>
<span class="small">(Dec 19, 2013)</span></li>
<li><a href="/news/spark-summit-2013-is-a-wrap.html">Spark Summit 2013 is a Wrap</a>
<span class="small">(Dec 15, 2013)</span></li>
<li><a href="/news/announcing-the-first-spark-summit.html">Announcing the first Spark Summit: December 2, 2013</a>
<span class="small">(Oct 08, 2013)</span></li>
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Related Projects:
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<ul class="list-narrow">
<li><a href="http://shark.cs.berkeley.edu">Shark (SQL)</a></li>
<li><a href="/streaming/">Spark Streaming</a></li>
<li><a href="/mllib/">MLlib (machine learning)</a></li>
<li><a href="http://amplab.github.io/graphx/">GraphX (graph)</a></li>
</ul>
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<div class="col-md-9 col-md-pull-3">
<div class="jumbotron">
<b>Apache Spark</b> is a fast and general engine for large-scale data processing.
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Speed</h2>
<p class="lead">
Run programs up to 100x faster than
Hadoop MapReduce in memory, or 10x faster on disk.
</p>
<p>
Spark has an advanced DAG execution engine that supports cyclic data flow and
in-memory computing.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="width: 100%; max-width: 272px; display: inline-block; text-align: center;">
<img src="/images/logistic-regression.png" style="width: 100%; max-width: 250px;" />
<div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
</div>
</div>
</div>
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<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<p class="lead">
Write applications quickly in Java, Scala or Python.
</p>
<p>
Spark offers over 80 high-level operators that make it easy to build parallel apps.
And you can use it <em>interactively</em>
from the Scala and Python shells.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="text-align: left; display: inline-block;">
<div class="code">
file = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
<br />
file.<span class="sparkop">flatMap</span>(<span class="closure">line => line.split(" ")</span>)<br />
.<span class="sparkop">map</span>(<span class="closure">word => (word, 1)</span>)<br />
.<span class="sparkop">reduceByKey</span>(<span class="closure">_ + _</span>)
</div>
<div class="caption">Word count in Spark</div>
</div>
<!--
<div class="code" style="margin-top: 20px; text-align: left; display: inline-block;">
file = spark.textFile(<span class="string">"hdfs://..."</span>)<br/>
<br/>
file.<span class="sparkop">filter</span>(<span class="closure">lambda line: "ERROR" in line</span>)<br/>
.<span class="sparkop">count</span>()
</div>
-->
<!--<div class="caption">Word count in Spark</div>-->
</div>
</div>
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<div class="col-md-7 col-sm-7">
<h2>Generality</h2>
<p class="lead">
Combine SQL, streaming, and complex analytics.
</p>
<p>
Spark powers a stack of high-level tools including
<a href="http://shark.cs.berkeley.edu">Shark</a> for SQL, <a href="/mllib/">MLlib</a> for machine learning,
<a href="http://amplab.github.io/graphx/">GraphX</a>, and <a href="/streaming/">Spark Streaming</a>.
You can combine these frameworks seamlessly in the same application.
</p>
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<area shape="rect" coords="0,0,74,95" href="http://shark.cs.berkeley.edu" alt="Shark (SQL)" title="Shark" />
<area shape="rect" coords="74,0,150,95" href="/streaming/" alt="Spark Streaming" title="Spark Streaming" />
<area shape="rect" coords="150,0,224,95" href="/mllib/" alt="MLlib (machine learning)" title="MLlib" />
<area shape="rect" coords="225,0,300,95" href="http://amplab.github.io/graphx/" alt="GraphX" title="GraphX" />
</map>
</div>
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<div class="col-md-7 col-sm-7">
<h2>Integrated with Hadoop</h2>
<p class="lead">
Spark can run on Hadoop 2's YARN cluster manager, and can read
any existing Hadoop data.
</p>
<p>
If you have a Hadoop 2 cluster, you can run Spark without any installation needed.
Otherwise, Spark is easy to run <a href="/docs/latest/spark-standalone.html">standalone</a>
or on <a href="/docs/latest/ec2-scripts.html">EC2</a> or <a href="http://mesos.apache.org">Mesos</a>.
It can read from <a href="http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html">HDFS</a>, <a href="http://hbase.apache.org">HBase</a>, <a href="http://cassandra.apache.org">Cassandra</a>,
and any Hadoop data source.
</p>
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<img src="/images/hadoop.jpg" style="width: 100%; max-width: 280px;" />
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-4 col-padded">
<h3>Community</h3>
<p>
Spark is used at a wide range of organizations to process large datasets.
You can find example use cases at the <a href="http://spark-summit.org/summit-2013/">Spark Summit</a>
conference, or on the
<a href="https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark">Powered By</a>
page.
</p>
<p>
There are many ways to reach the community:
</p>
<ul class="list-narrow">
<li>Use the <a href="/community.html#mailing-lists">mailing lists</a> to ask questions.</li>
<li>In-person events include the <a href="http://www.meetup.com/spark-users/">Bay Area Spark meetup</a> and
<a href="http://spark-summit.org/">Spark Summit</a>.</li>
<li>We use <a href="https://spark-project.atlassian.net">JIRA</a> for issue tracking.</li>
</ul>
</div>
<div class="col-md-4 col-padded">
<h3>Contributors</h3>
<p>
Apache Spark is built by a wide set of developers from over 25 companies.
Since the project started in 2010, more than 120 developers have contributed to Spark!
</p>
<p>
The project's
<a href="https://cwiki.apache.org/confluence/display/SPARK/Committers">committers</a>
come from 12 organizations.
</p>
<p>
If you'd like to participate in Spark, or contribute to the libraries on top of it, learn
<a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">how to
contribute</a>.
</p>
</div>
<div class="col-md-4 col-padded">
<h3>Getting Started</h3>
<p>Learning Spark is easy whether you come from a Java or Python background:</p>
<ul class="list-narrow">
<li><a href="/downloads.html">Download</a> the latest release — you can run Spark locally on your laptop.</li>
<li>Read the <a href="/docs/latest/quick-start.html">quick start guide</a>.</li>
<li>
Spark Summit 2013 contained free <a href="http://spark-summit.org/summit-2013/#day2">training videos</a> and <a href="http://spark-summit.org/2013/exercises/">exercises</a>
that you can run on Amazon EC2.
</li>
<li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster.</li>
</ul>
</div>
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<h3>Apache Incubator Notice</h3>
<p>
<small>
Apache Spark is an effort undergoing incubation at The Apache Software Foundation (ASF),
sponsored by the Apache Incubator. Incubation is required of all newly accepted projects
until a further review indicates that the infrastructure, communications, and decision
making process have stabilized in a manner consistent with other successful ASF projects.
While incubation status is not necessarily a reflection of the completeness or stability
of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
</small>
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Apache Spark is an effort undergoing incubation at The Apache Software Foundation.
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